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covers artificial intelligence, human-computer interaction, software and computer systems, and data science. The department is a dynamic and growing environment for research and education with around 300
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the Swedish Knowledge Foundation. In this position, you will belong to a research group with six senior researchers and several PhD students. The research focuses on managing data and information essential
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. The position aims to investigate the fundamental physical and chemical processes underlying the thermal-chemical conversion of green fuels, using advanced high-performance computational methods. The research
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Machine Learning Integration Develop and implement machine learning algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC
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provide detailed information on dynamic processes previously not feasible to study experimentally. Of special current interest is the development of new spectroscopic techniques to study high
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Postdoctoral studies, employing Bioinformatic gene-expression analysis in Neurobiology (scholarship)
-profile papers, with national and international collaborations. The ideal candidates should hold a PhD in Bioinformatics, Computational Biology or a related field, with experience in omics data analysis
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Postdoctoral studies, employing Bioinformatic gene-expression analysis in Neurobiology (scholarship)
-profile papers, with national and international collaborations. The ideal candidates should hold a PhD in Bioinformatics, Computational Biology or a related field, with experience in omics data analysis
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to improve our understanding of fundamental processes relevant to combustion engines, gas turbines, and fire safety. The successful candidate will join a dynamic and diverse research group with an extensive
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dynamic processes in atoms and molecules using electron spectrometers. The positions imply the development of ultra-short pulse technology at high repetition rate in new wavelength ranges, with the help
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algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC) to accelerate design iterations Integrate ML approaches with finite